truncreg: Truncated Regression Model

In oneinfl: Estimates OIPP and OIZTNB Regression Models

Truncated Regression Model

Description

Fits a positive Poisson (PP) or zero-truncated negative binomial (ZTNB) regression model.

Usage

truncreg(formula, df, dist = "negbin", start = NULL, method = "BFGS")

Arguments

formula	A symbolic description of the model to be fitted.
df	A data frame containing the variables in the model.
dist	A character string specifying the distribution to use. Options are "Poisson" or "negbin".
start	Optional. A numeric vector of starting values for the optimization process. Defaults to NULL, in which case starting values are attempted to be chosen automatically.
method	A character string specifying the optimization method to be passed to optim. Defaults to "BFGS".

Details

This function fits a regression model for zero-truncated counts. Zero-truncated models are used when the count data does not include zeros, such as in cases where only positive counts are observed.

The function supports two distributions:

• "Poisson": Zero-truncated Poisson regression.

• "negbin": Zero-truncated negative binomial regression.

The function uses numerical optimization via optim to estimate the parameters.

Value

An object of class "truncmodel" containing the following components:

beta

Estimated coefficients for the regression model.

alpha

Dispersion parameter (only for negative binomial distribution).

vc

Variance-covariance matrix of the estimated parameters.

logl

Log-likelihood of the fitted model.

dist

The distribution used for the model ("Poisson" or "negbin").

formula

The formula used for the model.

See Also

summary for summarizing the fitted model.

Examples

# Example usage
df <- data.frame(x = rnorm(100), y = rpois(100, lambda = 1) + 1)
model <- truncreg(y ~ x, df = df, dist = "Poisson")
summary(model)

oneinfl documentation built on April 4, 2025, 12:05 a.m.